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Resistance is Useless
Georg Simon Ohm and Modern Engineering Sciences
Georg Simon Ohm – Monument at TUM
Born on 16.3.1789 in Erlangen
Died on 6.7.1854 in Munich
Ohm Resistor
Ohm’s Law published
the Galvanic Chain – Mathematically Treated – 1827
Copley Medal – 1841
Member Royal Society London – 1842
Professor for Physics in Munich – 1849
Bavarian Academy of Sciences – 1850
Late Reconition in Germany
Ohm‘s Experiment
Silk Thread
Magnetic Needle
Magnifying Lense
Mercury Pads for Connecting Copper Wires
Copper
Bismuth
Ohm‘s Experiment
Experimental Setup – Schematically
Data Acquisition – Measurements
Copper wires
of length [inches]
2, 4, 6, 10 18, 34, 66 and 130
and of thickness 7/8 inch
The Model
Mathematical Model for Measurement Data
– Strength of magnetic force
– Length of copper wire
– Constant, only dependent on exciting force
– Constant, only dependent on the invariant part of the setup
Experimentally measured data
Cal Copper Wire
1 2 3 4 5 6 7 8
I 328 300 ½ 277 ½ 240 ¾ 190 ½ 134 ½ 84 ¼ 48 ½
II 313 287 ¼ 265 ½
230 ¼ 182 128 ½ 80 ¾ 46 ½
III 309 ½ 284 262 ½ 228 180 127 79 ¾ 45 ¾
IV 305 ½ 280 ½ 259 224 ¾ 177 ¾ 125 ¼ 79 45
V 305 ½ 280 ½ 259 224 ¾ 177 ¾ 125 ¼ 79 45
Ex Copper Wire
1 2 3 4 5 6 7 8
I 326 ¼ 300 ¾ 277 ¾ 238 ¼ 190 ¾ 134 ½ 83 ¼ 48 ½
II 311 ¼ 287 267 230 ¼ 183 ½ 129 ¾ 80 46
III 307 284 263 ¾ 226 ¼ 181 128 ¾ 79 44 ½
IV 305 ¼ 281 ½ 259 224 178 ½ 124 ¾ 79 44 ½
V 305 282 258 ¼ 223 ½ 178 124 ¾ 78 44
Data calculated from model
Ohm‘s Law
Scientific discovery in physics via
- empirical measurements followed by
- mathematical modeling
- no explanation of physical processes
Impact on Modern Engineering Sciences
Black-Box ModelModern Engineering Science
Inpu
tO
utput observablemeasurable
Abstract Input-Output Description
Example: Compressed Digital Video
Modeling Human Visual Perception
Video
Visual Quality
Data StreamEncoder
(Compression)
Encoder
(Compression)
Decoder
Control
Subjective Testing
Test persons judge visual quality of video entries in - Vector
Subjective testing
Main - Tempete - CIF
1
2
3
4
5
AVC_768 AVC_384 MP4_768 AVC_192 MP4_384 MP4_192 AVC_096 MP4_096
Black-Box Modeling
Find a (linear) model for input-output relation
measurable features in video u – variables
observable video quality y – variable
Vid
eo I
nput
Subj. Q
uality observablemeasurbale
Blur
Blocking
Noise
HVS
Black-Box Model for subjective image quality
Video
[Blur, Blocking, Detail, Motion, …,Noise] [Visual Quality]
Subjective testObjective Measurement
Video1
Video2
Video3
Video4
:
VideoN
Blu
r
Blo
ckin
g
Det
ail
Mo
tio
n
No
ise
PLSR, PCR …
Regression- techniques
(PCR ≈ SVD)
x x x x L xx x x x L xx x x x L xx x x x L xM M M M Mx x x x L x
⎡
⎣
⎢⎢⎢⎢⎢⎢⎢
⎤
⎦
⎥⎥⎥⎥⎥⎥⎥
y
y
y
y
y
Results
Thanks